This is very different from the structure preservation fashion of missing nodes as in SAT [1]} that we manually mask the edge between attribute-missing nodes on multiple graphs and enforce the network to focus more on these linkage relations. We summarize the merits of the fashion of our edge recovery: 1) more naturally evaluates the overall quality of restored attributes via structure refinement; 2) in turn enforces the resulting network to be able to selectively exploit more high-order discriminative information for data completion. Finally, the average structure reconstruction loss of all node pairs can be written as:
\( \mathcal {L}_{s} = \frac{1}{N^{2}}\sum _{i=1}^N \sum _{j=1}^N\mathcal {L}_{ij}.\) 
